






'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


UpperCamelCase     :   Dict	      =		[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
UpperCamelCase     :   Union[str, Any]	      =		[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

UpperCamelCase     :   Optional[int]	      =		(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCamelCase     :   Dict	      =		(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

UpperCamelCase     :   List[str]	      =		[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      str    ):
						for tf_name, hf_name in patterns:
												lowerCamelCase__								=	k.replace(__lowerCAmelCase ,     __lowerCAmelCase    )
						return k
def     A__      (					__lowerCAmelCase		:      dict ,     __lowerCAmelCase		:      dict    ):
						lowerCamelCase__								=	BigBirdPegasusConfig(**__lowerCAmelCase    )
						lowerCamelCase__								=	BigBirdPegasusForConditionalGeneration(__lowerCAmelCase    )
						lowerCamelCase__								=	torch_model.state_dict()
						lowerCamelCase__								=	{}

						# separating decoder weights
						lowerCamelCase__								=	{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""    )}
						lowerCamelCase__								=	{k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""    )}

						for k, v in tqdm(decoder_weights.items() ,     """tf -> hf conversion"""    ):
												lowerCamelCase__								=	[k.endswith(__lowerCAmelCase    ) for ending in KEYS_TO_IGNORE]
												if any(__lowerCAmelCase    ):
																		continue
												lowerCamelCase__								=	DECODER_PATTERNS
												lowerCamelCase__								=	rename_state_dict_key(__lowerCAmelCase ,     __lowerCAmelCase    )
												if new_k not in state_dict:
																		raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''    )
												if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]    ):
																		lowerCamelCase__								=	v.T
												lowerCamelCase__								=	torch.from_numpy(__lowerCAmelCase    )
												assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

						for k, v in tqdm(remaining_weights.items() ,     """tf -> hf conversion"""    ):
												lowerCamelCase__								=	[k.endswith(__lowerCAmelCase    ) for ending in KEYS_TO_IGNORE]
												if any(__lowerCAmelCase    ):
																		continue
												lowerCamelCase__								=	REMAINING_PATTERNS
												lowerCamelCase__								=	rename_state_dict_key(__lowerCAmelCase ,     __lowerCAmelCase    )
												if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
																		raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''    )
												if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]    ):
																		lowerCamelCase__								=	v.T
												lowerCamelCase__								=	torch.from_numpy(__lowerCAmelCase    )
												if k != "pegasus/embeddings/position_embeddings":
																		assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

						lowerCamelCase__								=	mapping["""model.embed_positions.weight"""]
						lowerCamelCase__								=	mapping.pop("""model.embed_positions.weight"""    )
						lowerCamelCase__							,      lowerCamelCase__								=	torch_model.load_state_dict(__lowerCAmelCase ,     strict=__lowerCAmelCase    )
						lowerCamelCase__								=	[
						    k
						    for k in missing
						    if k
						    not in [
						        """final_logits_bias""",
						        """model.encoder.embed_tokens.weight""",
						        """model.decoder.embed_tokens.weight""",
						        """lm_head.weight""",
						    ]
						]
						assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
						assert extra == [], F'''no matches found for the following tf keys {extra}'''
						return torch_model
def     A__      (					__lowerCAmelCase		:      List[Any]    ):
						lowerCamelCase__								=	tf.train.list_variables(__lowerCAmelCase    )
						lowerCamelCase__								=	{}
						lowerCamelCase__								=	["""global_step"""]
						for name, shape in tqdm(__lowerCAmelCase ,     desc="""converting tf checkpoint to dict"""    ):
												lowerCamelCase__								=	any(pat in name for pat in ignore_name    )
												if skip_key:
																		continue
												lowerCamelCase__								=	tf.train.load_variable(__lowerCAmelCase ,     __lowerCAmelCase    )
												lowerCamelCase__								=	array
						return tf_weights
def     A__      (					__lowerCAmelCase		:      str ,     __lowerCAmelCase		:      str ,     __lowerCAmelCase		:      dict    ):
						lowerCamelCase__								=	get_tf_weights_as_numpy(__lowerCAmelCase    )
						lowerCamelCase__								=	convert_bigbird_pegasus(__lowerCAmelCase ,     __lowerCAmelCase    )
						torch_model.save_pretrained(__lowerCAmelCase    )


if __name__ == "__main__":
							UpperCamelCase     :   Any	      =		argparse.ArgumentParser()
							parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
							parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
							UpperCamelCase     :   Union[str, Any]	      =		parser.parse_args()
							UpperCamelCase     :   List[Any]	      =		{}
							convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)


"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bool:




						'''simple docstring'''



						return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()


